Predictive Modeling in Healthcare- 3 Common Uses & How to Get Started

Predictive Modeling In Healthcare August 07, 2020

Chloe Henderson

Writer

While many healthcare industry professionals still believe that forecasting data analytics is only useful in industries that manage merchandise and customers, predictive modeling in healthcare is gaining momentum.

In fact, a Society of Actuaries' study shows that 89% of healthcare organizations are either using or planning to implement predictive analytics to enhance patient service.

Similar to how predictive models use machine learning to anticipate business risks and demands for retailers, analytics tools can identify inefficiencies and supply chain needs in the patient care routine. Through algorithms, big data, and patient information, the predictive model can also project patients' prognosis, behavior, and health risks.

These features allow healthcare providers to address risk factors and inadequate processes to develop customized patient experiences. This insight also helps in creating impactful marketing and outreach programs to target specific demographics.

The future for forecasting in healthcare is limitless. Therefore, health management officials should understand how predictive analytics can enhance patient care and decision-making.

3 Uses of Predictive Modeling in Healthcare

Analytics continues to grow in terms of capabilities and what it can offer patients, practitioners, and the health care system as a whole.

By aggregating data from historical and real-time information, such as health records and medical center logistics, predictive analytics can create relationships between values to detect critical patterns. These trends allow the prediction model to-

Predict Patient Traffic

With access to a facility's healthcare data, the predictive algorithm can identify patients that are more likely to miss their appointments based on their history. The analytics tool also monitors overall check-in and appointment trends to detect when there will be a surge of patient traffic.

As urgent medical centers utilize on-call practitioners, the staff can quickly become overwhelmed with patients without preparation. By using predictive models, hospitals can anticipate surges in traffic by adequately staffing the emergency and urgent care departments to ensure all patients are cared for.

This also gives inpatient facilities the opportunity to open enough rooms and beds to house admitted patients in advance. On the other hand, outpatient sites can prepare employees and processes to minimize patient wait time. By optimizing wait times and staffing, healthcare employees can boost patient satisfaction while ensuring employees are not overworked.

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Manage the Supply Chain

Even within the healthcare profession, the supply chain is one of the largest expenses for daily operations, and therefore, the most significant opportunity to save on spending.

Prediction models track ordering patterns, costs, and supply usage, to generate actionable insights on how management can reduce inventory costs. These evidence-based reports promote price negotiation tactics and minimizing supply variation to optimize economic stock replenishment.

A Navigant survey found that data analytics could cut supply chain costs by approximately 18%, saving hospitals nearly $10 million annually. Therefore, providers seeking to reduce unnecessary supply chain expenditures should utilize forecasting tools to aid in decision-making.

Simultaneously optimal replenishment of vital medical supplies will ensure all healthcare professionals are equipped with the proper tools, PPE, and resources to provide the best care possible to patients.

Optimize Data Security

Care centers must have strong cybersecurity as they deal with highly sensitive patient information. Predictive analytics and artificial intelligence have collaborated efforts to counteract security risks as attacks have become more sophisticated.

Using predictive analytics tools to monitor and regulate data sharing and authorized access allows the system to define patterns and trends. This enables the solution to alert users when there are early signs of security risks stemming from unusual metrics, such as excessive information exchange or unverified persons.

How to Start Using Predictive Analytics

To effectively utilize predictive analytics, healthcare organizations must start from the ground up, by establishing a stable infrastructure that can withstand data exchange and forecasting. Management can then begin their analytics project by following two simple steps-

1. Integrate Databases and Systems

Medical care centers handle a large patient population and therefore have multiple systems to regulate all relevant information. In order to filter all of this data into a predictive model, businesses must integrate all existing systems.

With an enterprise data warehouse (EDW), a universal platform is established where all solutions can send historical and real-time data. Analytics tools can then build upon this foundation to gain access to all data entries to generate the most accurate predictions and insights. This approach streamlines data exchange and generated reports by automating data entries, providing accurate predictions free of human error.

2. Use the 3 Steps of Predictive Modeling

Once solutions are properly integrated, the three steps of predictive modeling must be executed-

Carefully define the organization's goals, issues, concerns, and desired outcome. Once these needs are outlined, relevant data can be extracted from the integrated platform and evaluated to determine the appropriate algorithm.

Test all of the selected models with various data sets to determine which consistently achieves the desired outcome.

Once the test and quality checks are completed, the final predictive model can be implemented in the healthcare setting. However, management should perform regular maintenance in evaluations on the solution to ensure optimal performance.

Predictive analytics is continually evolving to provide healthcare professionals with the tools needed to anticipate fluctuations in supply chain functions and patient behavior. By implementing forecasting tools, organizations can ensure that staff and patients receive the best care while securing personal information and minimizing material expenses.